The amount of time that consumers spend on the Internet has steadily increased, as has the variety of web content, such that the Internet is often the first place many people turn to when searching for information, news, or entertainment. Consumers use a variety of methods to search for desired information on the Internet such as entering terms in a search engine. When a site of interest is found, users often times will bookmark the site to facilitate return visits. Over time, a user may develop a list of relevant sites based on a number of different topics. However, the constantly increasing number of websites has increased the time and effort it takes to weed through relevant websites.
Social networks provide another method for consumers to more quickly locate websites of interest. One example of social websites are social bookmark sites where users share their bookmarks with other users. The user will save bookmarks or tags associated with a web page of interest at the bookmark website. Users may also “tag” a website by associating a term or label with the website allowing the categorization of different sites based on the tag.
Thus, rather than using a search engine where software alone searches for a website based on content, social bookmark sites effectively use human beings (i.e., the users themselves) to rate and sort websites. Consequently, because a user found a webpage relevant enough to bookmark or tag, websites based on a particular topic are likely to be more relevant than software generated searches. Users may search other users' bookmarks based on the topic they are interested in to quickly locate relevant web sites.
In addition, the very nature of a user's bookmarking and tagging behavior inherently identifies a user's interest in particular topics—much more than current methods which rely on page content, often, a simple “keyword presence” or in some cases, a more sophisticated linguistic processing of the page the user is viewing. Furthermore, while the user may arrive at a page of interest, most techniques do little to “know” the actual intentions of the user. While there are some techniques that try to deduce actual intention by performing tracking on a user's past behavior, they do so on the basis of identifying which pages have already been browsed by the user, thereby assuming that viewing a page indicates significant personal interest in the topics on that page where no such significant interest may actually exist.